1. Field of the Invention
The present invention relates to a method for generating a block-based image histogram from data compressed by JPEG, MPEG-1, and MPEG-2, or uncompressed image data. In particular, the method employs block-based linear quantization to generate histograms that include color, brightness, and edge components.
2. Description of the Related Art
JPEG is the international standard for still images and MPEG-1, 2 are for moving pictures. Regarding the compressed image information, feature information is necessary for applications such as extracting key frames, searching images, and browsing.
To extract such feature information, a brightness and color histogram that express relative frequency of brightness and color (red, green, blue) in an image is widely used. Methods comparing histograms have been proposed for searching digital video applications. As histograms are used for searching images and detecting motion change, it is proposed that conventional histograms be improved. That is, conventional single component histograms with discrete quantization and color have been developed, and therefore composite histograms that employ linear update and soft decision are adopted for effective and efficient image description.
U.S. Pat. No. 5,805,733 “Method and system for detecting scenes and summarizing video sequence” disclosed a method that employs color histograms and edge maps for detecting motion change. Though the method is effective in that it extracts color information in consideration of the human eye, it doesn't use brightness information. A method disclosed by a technical paper “Color Indexing” published by International Journal of Computer Vision receives color information and measures similarity of images by histogram intersection method. However, this method doesn't use brightness information and therefore accuracy is not good enough. Also, since the conventional methods generate histograms using a discrete quantization method, a relatively large number of histogram bins are needed to achieve good performance. Consequentially, these methods are not efficient in terms of storage and similarity measurement.
In addition, because the conventional methods perform feature extraction in terms of pixel in generating histograms, feature information is very restrictively generated.